There are various artifacts that are characteristic to computed tomography (CT) technology, for example, such as in cone beam CT (CBCT) and spiral x-ray CT. In one paper (“Artifacts in Spiral X-ray CT Scanners: Problems and Solutions”, Proceedings Of World Academy Of Science, Engineering and Technology, Volume 26, December 2007, pp. 376-380) researcher Mchran Yazdi describes three classes of artifacts: physics-based, patient-based, or scanner-based artifacts. Physics-based artifacts, for example, can be caused by beam hardening, photon starvation, and under-sampling. Patient-based artifacts can include artifacts caused by high density objects and inadvertent motion. Scanner-based artifacts include those caused by detector sensitivity and mechanical instability.
Artifacts caused by metal and other high density materials pose a significant problem that affects the performance of computed tomography (CT) systems. Metal features tend to generate high-frequency streaks or artifacts in the resulting image, typically emanating from metal objects in the scanned subject. These artifacts could occur due to high attenuation by the metal objects and consequent reduction in the number of photons reaching the detector of the CT system. This can also result in a poor signal-to-noise ratio. Additionally, metal objects harden the x-ray beam by attenuating x-rays in an energy-specific manner. The resulting nonlinear changes in the projection data can appear as low-frequency tail artifacts around the metal objects, as well as between the metal and other high density objects. For example, in medical diagnostic imaging, streaks caused by implanted metal objects limit the capability to assess surrounding soft tissues and skeletal structures. In dental cone beam CT imaging, artifacts cased by dental fillings (including dental fixtures) can constrain or prevent proper representation of surrounding tooth, bone, and tissue structures. Metal and other high density features attenuate x-ray beams as they propagate through the patient or other subject being exposed, complicating the task of accurate 3-D reconstruction and often resulting in unwanted image artifacts.
Various approaches have been considered to mitigate the effects of high density objects in CT reconstruction. One simple preventive solution has been to use filling and restorative materials that have lower X-ray attenuation coefficients and to develop and use implants and other devices that have smaller cross-sectional areas. Another approach is increasing the X-ray energy to improve beam penetration and to reduce effects of the missing projection data resulting from high density features. These approaches can help to reduce/minimize the impact of Metal and other high density features, but may not be appropriate in all cases. Increasing X-ray energy, because of increased risk to the patient, is seen as a poor solution to the problem.
Image processing methods have been developed to address the artifact problem. One method for addressing artifacts due to high density features is to reformat the axial CT image data into new interpolated axial, orthogonal, or oblique images. Image reformatting into planes other than the scan plane can weight the true image signal over the pseudo randomly distributed artifact signal when integrating between adjacent axial images (original axial images are averaged out of the planar reformatting). As another processing solution, post-reconstruction filtering can also be directly applied to noisy images to improve image quality.
Image processing methods that have been considered for reducing artifacts and re-creating the missing projection data can be generally classified into two categories: projection interpolation and iterative reconstruction. As one example of the latter approach, U.S. Pat. No. 7,023,951 entitled “Method and Apparatus for Reduction of Artifacts in Computed Tomography Images” to Man describes a method for reducing artifacts in CT images by iteratively reconstructing corrected sinogram data to generate improved reconstructed CT images based on a weight measure associated with each sinogram element. The corrected sinogram is generated by, for example, interpolating a measured sinogram that is the original sinogram obtained from the CT scanner.
Non-iterative sinogram interpolation techniques have also been proposed for addressing this problem. For example, U.S. Pat. No. 6,721,387 entitled “Method and System for Reducing Metal Artifacts in Images Generated by X-ray Scanning Devices” to Naidu et al. describes identifying metal objects in preliminary images, generating metal projections from the identified metal objects, then subtracting the metal projections from the input projections to yield corrected projections. The final corrected image is then reconstructed from the corrected projections. Naidu et al. also describe preserving thin-sheet high density objects in the '387 patent disclosure.
Conventional image processing methods that address the metal artifact problem use information extracted from neighbors of the high density objects identified to mitigate the artifacts. While such methods may have some merit, however, they fall short of what is needed for accurate artifact compensation for cone-beam CT dental images. A number of problems encountered in dental CT imaging are particular to dental imaging applications and are not encountered for other imaging applications. For example, unlike other CT imaging applications, dental CT imaging encounters a range of hard and soft tissue types, such as hone, dentine, enamel, and gum tissue, as well as a variety of metals and other high density materials used in fillings, implants, crowns, and other restorative structures. Interpolation methods such as those proposed in the Naidu et al. '387 disclosure can be employed when metal objects are embedded within a substantially homogenous area, but yield disappointing results in the dental imaging environment where metal objects or fillings reside in regions that have dramatically different properties than the surrounding tissues. In general, automated procedures for metal detection can be computationally intensive and may not utilize relevant information about the patient in order to optimize operation.
Thus, Applicants recognize that there is a need for a method that compensates for artifacts due to metal and other high density materials in CBCT and other CT scanning, particularly dental CT scanning.